Extracts statistically independent components from data. Only affects numerical features. See fastICA::fastICA for details.

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpICA$new(id = "ica", param_vals = list())

`id`

::`character(1)`

Identifier of resulting object, default`"ica"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

Input and output channels are inherited from `PipeOpTaskPreproc`

.

The output is the input `Task`

with all affected numeric parameters replaced by independent components.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

, as well as the elements of the "fastICA" function,
with the exception of the `$X`

and `$S`

slots. These are in particular:

`K`

::`matrix`

Matrix that projects data onto the first`n.comp`

principal components. See`fastICA()`

.`W`

::`matrix`

Estimated un-mixing matrix. See`fastICA()`

.`A`

::`matrix`

Estimated mixing matrix. See`fastICA()`

.`center`

::`numeric`

The mean of each numeric feature during training.

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as the following parameters
based on `fastICA()`

:

`n.comp`

::`numeric(1)`

Number of components to extract. Default is`NULL`

, which sets it to the number of available numeric columns.`alg.typ`

::`character(1)`

Algorithm type. One of “parallel” (default) or “deflation”.`fun`

::`character(1)`

One of “logcosh” (default) or “exp”.`alpha`

::`numeric(1)`

In range`[1, 2]`

, Used for negentropy calculation when`fun`

is “logcosh”. Default is 1.0.`method`

::`character(1)`

Internal calculation method. “C” (default) or “R”. See`fastICA()`

.`row.norm`

::`logical(1)`

Logical value indicating whether rows should be standardized beforehand. Default is`FALSE`

.`maxit`

::`numeric(1)`

Maximum number of iterations. Default is 200.`tol`

::`numeric(1)`

Tolerance for convergence, default is`1e-4`

.`verbose`

`logical(1)`

Logical value indicating the level of output during the run of the algorithm. Default is`FALSE`

.`w.init`

::`matrix`

Initial un-mixing matrix. See`fastICA()`

. Default is`NULL`

.

Uses the `fastICA()`

function.

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

Other PipeOps: `PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTaskPreproc`

, `PipeOp`

,
`mlr_pipeops_boxcox`

,
`mlr_pipeops_branch`

,
`mlr_pipeops_chunk`

,
`mlr_pipeops_classbalancing`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_classweights`

,
`mlr_pipeops_colapply`

,
`mlr_pipeops_collapsefactors`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`mlr_pipeops_encode`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_filter`

,
`mlr_pipeops_fixfactors`

,
`mlr_pipeops_histbin`

,
`mlr_pipeops_imputehist`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputenewlvl`

,
`mlr_pipeops_imputesample`

,
`mlr_pipeops_kernelpca`

,
`mlr_pipeops_learner`

,
`mlr_pipeops_missind`

,
`mlr_pipeops_modelmatrix`

,
`mlr_pipeops_mutate`

,
`mlr_pipeops_nop`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa 1.4 0.2 5.1 3.5 #> 2: setosa 1.4 0.2 4.9 3.0 #> 3: setosa 1.3 0.2 4.7 3.2 #> 4: setosa 1.5 0.2 4.6 3.1 #> 5: setosa 1.4 0.2 5.0 3.6 #> --- #> 146: virginica 5.2 2.3 6.7 3.0 #> 147: virginica 5.0 1.9 6.3 2.5 #> 148: virginica 5.2 2.0 6.5 3.0 #> 149: virginica 5.4 2.3 6.2 3.4 #> 150: virginica 5.1 1.8 5.9 3.0#> Error: The following packages could not be loaded: fastICApop$state#> NULL